Bank and sovereign risk feedback loops



Working Paper Series | 1 | 2015

Bank and sovereign risk

feedback loops

This paper studies the link between

sovereign risks and the fragility of the

banking sector pointing to the challenges

of bank rescue operations for the state.

Aitor Erce


This Working Paper should not be reported as representing the views of the ESM.

The views expressed in this Working Paper are those of the author(s) and do not

necessarily represent those of the ESM or ESM policy.

Working Paper Series | 1 | 2015

Bank and sovereign risk feedback loops

Aitor Erce 1


Measures of sovereign and bank risk show occasional bouts of increased correlation, setting the stage

for vicious and virtuous feedback loops. This paper models the macroeconomic phenomena underlying

such bouts using CDS data for 10 euro area countries. The results show that sovereign risk feeds back

into bank risk more strongly than vice versa. Countries with sovereigns that are more indebted or where

banks have a larger exposure to their own sovereign, suffer larger feedback loop effects from sovereign

risk into bank risk. In the opposite direction, in countries where banks fund their activities with more foreign

credit and support larger levels of non-performing loans, the feedback from bank risk into sovereign risk is

stronger. According to model estimates, financial rescue operations can increase feedback effects from

bank risk into sovereign risk. These results can be useful for the official sector when deciding on the form

of financial rescues.

Key words

Sovereign Risk, Bank Risk, Feedback Loops, Balance Sheet Exposure, Leverage

JEL codes

E58, G21, G28, H63

I thank Antonello D’Agostino, Gong Cheng, Jon Frost, Patricia Gomez, Carlos Martins, Tomasz Orpiszewski, Cheng PG-Yan, Chander

Ramaswamy, Juan Rojas, Karol Siskind and seminar participants at the European Stability Mechanism and the 2014 Symposium

of Economic Analysis for their suggestions, and Sarai Criado, Gabi Perez-Quiros and Adrian Van Rixtel for sharing their CDS data.

Assunta Di Chiara provided outstanding research assistance.


European Stability Mechanism. The author can be contacted at:


This Working Paper should not be reported as representing the views of the ESM. The views expressed in this Working Paper are

those of the author(s) and do not necessarily represent those of the ESM or ESM policy.

No responsibility or liability is accepted by the ESM in relation to the accuracy or completeness of the information, including any

data sets, presented in this Working Paper.

© European Stability Mechanism, 2015 All rights reserved. Any reproduction, publication and reprint in the form of a

different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written

authorisation of the European Stability Mechanism.

ISSN 443-5503

ISBN 978-92-95085-06-0

DOI 10.2852/501718

EU catalogue number DW-AB-15-001-EN-N


As the global crisis engulfed a number of economies into a perverse spiral of …scal

and …nancial distress, the interconnectedness between banks and sovereigns

has attracted increasing attention. On the one hand, a number of countries

faced severe banking crises, whose management contributed to the subsequent

…scal crisis. Arguably, this is what happened to Iceland, where the materialization

of contingent claims brought havoc onto the sovereign’s balance sheet. 1 On

the other hand, pro-cyclical …scal policy and a lack of competitiveness led to a

sovereign debt crisis in Greece. As foreign investors withdrew, banks became

major holders of public debt (Broner et al., 2014). Successive sovereign downgrades,

ending in a sovereign debt restructuring, contributed to the collapse

of the Greek banking sector. Against this background, this paper uses euro

area data to extract lessons about the processes through which sovereigns and

banks interlink. In order to do so, this paper provides a framework that relates

the joint dynamics of …scal credit risk (Sovereign Risk) and banking credit risk

(Bank Risk) to di¤erent underlying vulnerabilities and shocks. The analysis delivers

an understanding of what conditions facilitate the emergence of feedback

loops between sovereign and bank risk.

A number of recent contributions study this two-way relationship by modelling

the common dynamics of bank and sovereign Credit Default Swaps (CDS)

spreads using vector-auto regression models as in Diebold and Yilmaz (2009).

According to Moody’s (2014), which studies the dynamic relation between sovereign

and bank CDS spreads by means of a Markov switching VAR methodology,

the euro area did not su¤er one …nancial crisis, but a variety of crises,

each of them with its own speci…cities. According to their results, only Ireland

witnessed a spillover of …nancial stress into sovereign stress. Instead, for Greece

and Italy their results point to the opposite feedback e¤ect. For the rest of the

countries analyzed, stress feeds back in both directions. These time series techniques

deliver interesting indices of contagion but fall short of describing the

actual channels through which such bouts of contagion take place. To bridge

this gap, this paper provides a framework conditioning the intensity of the feedback

loops on di¤erent economic factors. In doing so, similar to Acharya et al.

(2013) or Mody and Sandri (2011), this paper delivers an understanding of the

vulnerabilities and shocks that are fertile ground for the emergence of vicious

spirals of increasing sovereign and bank risk. 2

To provide estimates of how credit risk interconnectedness varies with the

economic environment, the analysis uses detailed information on the state of

public …nances, the banking system and the macro economy. The paper presents

a simple econometric strategy to assess whether the sensibility of the feedback

between bank and sovereign risk varies with these indicators. Given the low frequency

of macroeconomic variables and the short time series available for CDS

1 In Iceland, bank failures directly increased net public debt by 13% of GDP (Carey, 2009).

2 Heinz and Sun (2014) or Delatte et al. (2014) show the presence of non-linearities on

sovereign risk pricing.


data, the paper relies on panel data econometrics. In addition to a generalisedleast-squares

estimator, motivated by the high persistence of the CDS series,

dynamic models are also used. The framework provides a quantitative benchmark

to measure the impact on sovereign risk of bank rescue measures, as those

enacted by euro area governments between 2007 and 2013. Understanding the

sensitivity of sovereign risk to such policies is, given the Euro Area policy setting,

fundamental. 3

The main …ndings are the following. There is a strong pass-through of

sovereign risk on bank risk. Moreover, the sovereign feedback e¤ect is quantitatively

stronger when increases in sovereign risk occur in countries with a

larger stock of public debt, when the banking system exposure to the sovereign

is large or when the sovereign has lost its investment grade rating. There is also

evidence of positive spillovers from bank risk into sovereign risk. In this case,

however, signi…cant pass-through appears only under speci…c macroeconomic

environments and is signi…cantly smaller. Bank risk spillovers are signi…cantly

stronger in countries where banks have bigger balance sheets and where the

volume of non-performing loans and foreign liabilities is larger. As regards the

role of bank rescues, the results show that such policy operations can facilitate

the appearance of strong feedback e¤ects.

The next section summarizes the main channels through which distress

spreads, as documented in the literature. The following one describes the data

and presents some preliminary evidence. The next describes the econometric

strategy and details the main results from the analysis. The section also

presents a detailed analysis of the e¤ect on the feedback between risks of the

bank bailouts designed in Europe during the crisis. The …nal section concludes.

1 Literature review: what are the channels of


In order to guide the analysis and help clarifying the choice of variables for

carrying out the empirical exercise, this section discusses the most relevant

channels through which …nancial and …scal stress intertwine, as identi…ed in

literature. 4 These channels include the direct balance sheet interconnection, as

well as other indirect ways through which underlying vulnerabilities in either

the banking or public sector may materialize into twin crises.

3 The European Banking Union aims to delink sovereigns and banks by allowing for bank

recapitalisation funded at the European level whenever a bank rescue risks overburdening the

national …scal position.

4 Reinhart and Rogo¤ (2012) show that (i) private and public debt booms ahead of banking

crises, (ii) banking crises, both home-grown and imported, often accompany sovereign debt

crises and, (iii) public borrowing increases sharply ahead of debt crises and (iv) it turns out

that the government has “hidden debts” (domestic public debt and contingent private debt).

Closely related, Balteanu and Erce (2014) show that twin sovereign debt and banking crises

in emerging countries always combine with boom-bust patterns on the banking system.


A number of recent contributions study the two-way feedback between Sovereign

and Bank stress by studying the common dynamics of bank and sovereign

CDS spreads using vector-auto regression models (following the methodology

proposed by Diebold and Yilmaz (2009). While these models are extremely

useful to understand the joint dynamics of the series, as they rely fully on the

time series dimension, they provide no economic guidance on the drivers of the

feedback e¤ects. In order to gauge an idea on the speci…c mechanisms through

which stress transmits, the literature has relied, instead, on pooling country

data together. Heinz and Sun (2014) use a generalized least squares panel data

approach to analyze sovereign CDS drivers. They show that global factors account

for a relevant portion of the observed variation. Acharya et al. (2013)

present cross-country evidence about the potential for bank bailouts to trigger

a …scal crisis. Their narrative of the crisis presents three di¤erentiated periods.

They portray a …rst period, extending until 2007, in which sovereign risk

was never an issue within the euro area. Then, starting with the …rst bank

bailouts in 2008, sovereign risk starts to surface in some parts of the Monetary

Union as economic prospects deteriorate and public debt raises on the back of

the support provided to a seriously deteriorated …nancial system. Since 2010,

sovereign risk has become the major concern and, for some countries, implied a

resurfacing of concerns regarding …nancial risk, due to the fact that a number of

banks were either heavily exposed to the sovereign (Bruegel, 2012) or su¤ered

from the lowering of the public guarantees provided to them (BIS, 2010). The

empirical analysis in Acharya et al. (2013) relies on the use of CDS spreads and

relates their co-movement to resolution policies and macro factors. Their results

show that the bailout led to an increase in sovereign risk. Moreover, they show

that, even after controlling for bank-speci…c and macroeconomic variables, the

contemporaneous relation between sovereign and bank CDS spreads remain,

con…rming the existence of a sovereign bank loop. Closely related, Thukral

(2013) uses a panel data framework with lagged regressors to study the role of

…nancial sector variables on the determination of sovereign CDS spreads. He

constructs a bank risk index using bank CDS spreads and …nds that the index is

the primarily statistically signi…cant determinant of sovereign risk premia even

when …scal variable are included, which he characterizes as bank dominance of

sovereign …nancing conditions. Mody and Sandri (2011) recognize the existence

of broadly similar sub-periods as Acharya et al. (2013), in which the feedback

between sovereign and bank risk changed. Instead of comparing CDS spreads,

Mody and Sandri (2011) focus on sovereign spreads as a measure of the …scal

risk, and banks’ stock market capitalization as a measure of risk within the

banking system. Their results, using spreads and market valuations, show that

the euro crisis traces back to the demise of Bear Stearns. They argue that under

the weight of increasing support for banks, sovereign spreads started to rise,

especially in countries with weak growth prospects and high debt levels.

Another literature strand has delved into the role of monetary policy in

strengthening the vicious relation between sovereign and bank risk. According

to Darraq-Pires et al. (2013), the ECB’s full-allotment liquidity policy is an


e¢ cient tool to stabilize spiralling feedback loops between banks and the …scal

authorities. Drechsler et al. (2013) study the reasons behind the heterogeneous

take up of long-term re…nancing operations (LTROs) among European banks.

They document that banks where this take up was larger also featured larger

increases in their sovereign debt exposure. 5 Drechsler et al. (2013) de…ne a

haircut subsidy associated with using government bonds as collateral with the

ECB, as opposed to government bonds in private repo markets. Using this

subsidy, they provide support for the hypothesis that ECB collateral policies

action help explain the increased balance sheet interconnection between banks

and sovereigns in the euro area.

As regards the main transmission channels from bank stress to the sovereign,

Candelon and Palm (2010) highlight four. First, rescue plans may impair the

sustainability of public …nances. 6 They can include bailout money, government

deposits, liquidity provisioning by the central bank, public recapitalization and

the materialization of public guarantees. 7 Second, if contingent liabilities materialize,

…scal costs are likely to be substantial. Next, the risk premium increases

even if guarantees remain unused, raising borrowing costs for both the sovereign

and the private sector (sovereign ceiling). 8 Last, the downturn originated by the

credit crunch accompanying the …nancial crisis can deepen the recession, leading

to further falls in public revenues, deepening the de…cit and driving up debt.

King (2009) provides an event analysis on the impact of government guarantees

on the banking system using the battery of bank rescues that took place in late

2008. According to his results, the bailouts bene…ted the banks’creditors, as

re‡ected in falling bank CDS spreads, at the expense of equity holders, given

that banks’stock underperformed vis-a-vis the market.

If …nancial turmoil negatively in‡uences asset prices, unemployment and

output, the direct costs increase by the impact of the crisis on tax collection and

public expenditure. Baldacci and Gupta (2009a, 2009b) argue that sovereign

debt distress (deterioration of the …scal position) after a banking crisis is likely

to occur due to a combination of lower revenues and higher expenditures (bank

rescues and outlays associated with the downturn). 9 According to Honohan

(2008), banking crises last 2.5 years on average, public debt increases by around

30% of GDP and their estimated median …scal cost stands at 15.5% of GDP.

Distress can also spread through the credit crunch created by the …nancial

5 Acharya and Tuckman (2013), using data for broker-dealers in the US, show that Lender of

Last Resort activities can have the perverse side e¤ect of slowing down deleveraging, increasing

illiquid leverage and the risk of default.

6 Rosas (2006) studies the drivers of government intervention after banking crises. He …nds

that authorities are more likely to bailout failing institutions in open and rich economies or

if …nancial turmoil was caused by regulatory issues. On the other hand, electoral constraints

and central bank independence seem to favor bank closure.

7 See Feenstra and Taylor (2008) or Reinhart and Rogo¤ (2011).

8 Laeven and Valencia (2011) show that blanket guarantees increase the …scal costs of

banking crises, but this can also be because they are set in place during severe crises.

9 Baldacci and Gupta (2009) argue that …scal expansions do not improve the growth outlook

by themselves and lead to higher interest rates on long-term government debt. They identify a

trade-o¤ between boosting aggregate demand (short-run) and productivity growth (long run).


crisis. As credit falls or becomes more expensive, the economy is likely to

su¤er a drop in GDP growth. This might put additional pressure on the …scal

position through its impact on tax revenues, likely to be lower as activity falls. 10

Relatedly, Laeven and Valencia (2011) focus on the impact of …nancial sector

interventions on the capacity of the …nancial system to provide credit. Their

results show that …rms dependent on external …nancing bene…ted signi…cantly

from bank recapitalization operations. However, as documented in Acharya, if

the sovereign becomes overburdened, the value of the public guarantees falls,

deepening the interconnection of stress. Kollmann et al. (2012) also focus

on the impact of bank rescues. Their message is positive and highlights the

ability of bank rescue operations to improve macroeconomic performance. Still,

while they show that bank rescues raise investment, in line with the evidence in

Broner et al. (2014) or Popov and Van Horen (2013), they …nd that sovereign

debt purchases by domestic banks lead to a crowding out of private investment.

Gray and Jobst (2011) and Gray et al. (2013) present a less benign exercise

showing the potentially high impact on …scal risk associated to the existence of

contingent liabilities.

Finally, if uncertainty augments the crisis could lead to a sudden stop of

capital in‡ows. In this line, Reinhart and Rogo¤ (2008) argue that banking

crises often follow credit booms and high capital in‡ows. Moreover, they …nd

that periods of high international capital mobility gave rise to banking crises

in the past. Cavallo and Izquierdo (2009) provide further evidence showing

that, after …nancial crises in emerging markets, capital ‡ows may collapse for

months or years potentially triggering a solvency crisis. Indeed, as argued by

Obstfeld (2011) when discussing the role of international liquidity in the recent

debt crisis, “. . . gross liabilities, especially those short-term, are what matter”.

Van Rixtel and Gasperini (2013) show that sovereign risk, as measured by the

sovereign swap spreads, has shown in some periods a strong correlation with the

three-month USD Libor-OIS, a sign that borrowing strains in foreign currency

for banks a¤ect the creditworthiness of the sovereigns.

In turn, a number of transmission channels of a …scal crisis on the broader

economy can be traced through the domestic …nancial system. 11 Whenever

assets need to be written o¤ or rescheduled, domestic banks are usually the

…rst in line to take a hit. Along these lines, Noyer (2010), argues that banks’

holdings of defaulted government bonds might lead to large capital losses and

threaten the solvency of elements of the banking sector. IMF (2002) provides a

comprehensive overview of the e¤ects of four sovereign restructurings (Ecuador,

Pakistan, Russia and Ukraine) on the domestic banking sector. The paper documents

the extent of direct losses from banks’holdings of government securities,

an increase in the interest rates on liabilities not matched by increased returns

on assets (on the contrary, in this context government securities usually o¤er

non-market rates), and an increase in the rate of non-performing loans increases,

as higher …nancing costs lead to corporate bankruptcies. Similarly, Erce (2012)

10 See De Paoli et al. (2009) or Feenstra and Taylor (2008).

11 See IMF (2002) or Reinhart and Rogo¤ (2012).


suggests that the degree of bank intermediation and the banking system exposure

to the sovereign strongly in‡uence a debt crisis ripple e¤ect on the real

economy. In addition, authorities often react to debt problems by coercing domestic

creditors to hold government bonds in non-market terms (Diaz-Cassou

et al., 2008). 12 While this keeps borrowing costs low, a government default may

trigger a banking crisis. 13 In Darraq-Pires et al. (2013) the positive connection

between sovereign and bank risk is due to banks investing in government

securities to hedge future liquidity shocks. Along these lines, Angeloni and

Wol¤ (2012) assess the impact of sovereign bond holdings on the performance

of banks during the euro area crisis using individual bank data and sovereign

bond holdings. They …nd that peripheral sovereign bonds a¤ect banks’stock

market valuations heterogeneously. While Italian, Irish and Greek debt appear

to have negatively a¤ected the market valuation of the banks holding them,

such an e¤ect is not signi…cant for other peripheral sovereign debt, most notably,

Spanish. 14 Acharya et al. (2013), document the high exposure of their

sample banks to their own sovereign, which according to their theory should be

a main channel through which stress feeds back. 15

Beyond this direct balance sheet e¤ect, the ensuing …scal contraction may

lead to reduced activity, a¤ecting banks’pro…ts and further damaging the …nancial

system. Moreover, a credit crunch may worsen the economic downturn, as

banks reduce lending due to capital losses and to the increase in uncertainty that

comes with a sovereign debt default (Panizza and Borenzstein, 2008). Popov

and Van Horen (2013) focus on the feedback from sovereign risk into banking

risk by assessing the extent to which increasing holdings of distressed sovereign

bonds limit the banks’ability to extend loans to the private sector, furthering

the vicious feedback loop by limiting the growth potential of the economy. They

document a stronger reallocation away from domestic lending in the periphery.

A similar crowding out e¤ect is present in Broner et al. (2014), who present a

battery of stylized facts for the euro area, including both an increase in sovereign

bond holdings by banks and a simultaneous drop in …nancing to the private sector.

16 Corporate borrowers and banks may face a sudden stop after a sovereign

default even if their exposure to government bonds is limited. Gennaioli et al.

(2010) and Erce (2012) argue that sovereign defaults trigger capital out‡ows

and credit crunches. An additional pressure to curtail lending might come from

12 Das et al. (2012) argue that regulatory factors could lead to further balance sheet intertwining.

In Livshits and Schoors (2009), as public debt becomes risky, governments have

incentives to not adjust prudential regulation.

13 In past crises, prudential regulation treated government bonds as risk-free despite default

expectations were not zero (IMF, 2002). According to Castro and Mencia (2015), a similar

phenomenon has been at play in the Eurozone

14 A caveat of this analysis is that data stops in mid-2012, before the height of stress in Italy

and Spain.

15 Among other things, the paper assesses the extent to which reduced sovereign ratings

a¤ected the banks CDS through their e¤ect on the public guarantees.

16 These papers present a nuanced view of domestic purchases of public debt. Others have

found positive e¤ects. According to Asonuma et al. (2015) and Andritzky (2012), domestic

bank purchases of sovereign bonds help stabilize sovereign funding costs.


the fact that the economic uncertainty may lead to deposit runs or a collapse

of the inter-bank market (Panizza and Borenzstein, 2008). Finally, sovereign

rating downgrades further limit banks’ access to foreign …nancing, leading to

sudden stops or higher borrowing costs (Reinhart and Rogo¤, 2012).

2 Data

On the sovereign front, some authors have measured credit risk using credit

ratings (Correa et al., 2012) or bond spreads (Mody and Sandry, 2011). In turn,

bank risk proxies previously used include credit ratings (Correa et al., 2012) and

the stock market behavior (Angeloni and Wol¤, 2012). The analysis here follows

a recent strand of the literature that has opted for using credit default swaps

(CDS). By design, CDS contracts shield the holders from events of default, so

are the …nancial instruments most related to credit risk. Importantly, although

the data spans back a little less than a decade, CDS markets are relatively

liquid. 17 Monthly data for 5-year CDS contracts for both individual banks

and sovereigns comes from Bloomberg and DataStream. For sovereign CDS

data, in most countries the information spans back to late 2005. In order to be

able to assess the various twists observed during the crisis, countries for which

sovereign CDS data was missing prior to 2008 (Cyprus and Luxembourg) were

excluded from the sample. In turn, the above-cited sources returned active

CDS contracts for 48 banks in the euro area. Unfortunately, prior to 2007,

the coverage was less homogeneous. When considering together the coverage of

both banks and sovereign entities, su¢ ciently large series were available for 10

euro area countries: Germany, Italy, France, Spain, Ireland, Greece, Portugal,

Belgium, Netherlands and Austria. 18

As in Acharya et al. (2013), to have a system-wide measure of bank stress,

individual bank CDS data is aggregated in a country-speci…c bank risk index.

De…ning the CDS of bank j 2 J from country i at time t by Bank CDS jit and

the corresponding weight as w jit ,country’s i Bank Risk Index is

BankRisk it = X 8j2J

w jit BankCDS jit

From the various weighting schemes available, for simplicity, this paper uses

w jit = 1 J :19

The econometric exercise controls for various macroeconomic, …nancial and

global factors. Data on sovereign ratings comes from Fitch. Data on the banks’

balance sheets come from Haver Analytics, the European Central Bank, the

17 An important limitation of CDS data relates to the existence of counterparty risk. The

lack of detailed data on CDS counterparties prevents from controlling for this potential bias.

18 There is no CDS data for Finnish banks, preventing its inclusion in the analysis.

19 Banks weights could be set according to their market capitalization or total assets. While

the …rst option above focuses on private capital, depending on the extent of bank nationalisation,

the second can be more adequate.


Bank for International Settlements and the IMF’s Financial Stability Indicators.

20 The series included are: total assets, exposure to the general government,

funding from the central bank, foreign assets and liabilities, non-performing

loans, return on assets and equity ratio. Macroeconomic data (unemployment,

in‡ation, nominal GDP growth, …scal de…cit, current account and public debt)

was obtained from Haver Analytics. 21 The Itraxx …nancial Junior and VIX

index come from Bloomberg.

3 Preliminary Evidence

Figures 1 and 2 (in the Appendix) provide a bird’s eye view on the behavior of

the risk series. Figure 1 portrays the behavior of sovereign and bank risk from an

aggregate perspective. Euro area wide sovereign stress is proxied using a simple

average of sample countries’sovereign CDS. The Itraxx Junior represents bank

risk. In turn, Figure 2, shows the behavior of sovereign and bank on a countryby

country basis.

As a reminder of the importance of policy action, the shadowed areas in

Figure 1 represents two periods of marked policy activism. The …rst depicts

the two months of 2008 in which most sample countries enacted programs of

support for their …nancial systems. Remarkably, even at the low frequency

employed here, the very speci…c dynamics ongoing during the third quarter of

2008 are still apparent. On the back of the public guarantees, the bank credit

risk decreased markedly. However, simultaneously, the sovereign CDS started

to pick up. According to Acharya et al. (2013), the increasing sovereign CDS

re‡ected market fears regarding the just absorbed liabilities. The second period

shadowed in Figure 1 corresponds to that following the ECB announcement of

the Outright Monetary Transactions (OMT) instrument (August 2012). While

it is not apparent that such policy action changed the correlation, Figure 1

shows a change in risk dynamics. Since then, both risk indicators have trended

down. Another way to look at time patterns for the correlation between the risk

variables comes from comparing sub-periods. This is done in Table 1 below.

20 IMF’s FSI indicators (non-performing loans, return on assets and equity ratio) are available

only since 2008.

21 Converse to the literature on sovereign spreads that focuses on real GDP, nominal GDP

is used given its relevance in markets’assessment of debt sustainability. The debt and …scal

data refers to the General Government. These variables, as GDP, are available only on a

quarterly basis. They have been linearly interpolated into monthly frequency.


In periods 2 and 3 (bail-out and …scal activism), the correlation observed

previously broke down. Remarkably, since the inception of the OMT, the correlation

is back to its pre-crisis value. 22 Following, Broner et al. (2014) narrative

of the crisis, further insights into the dynamic relation of the risk indicators can

be gained by breaking the euro area into a core and a periphery. This is done

by running the following regressions

Risk_A it = i + X

Risk_A it = i +


p P eriod p dummy Risk_Z it 1 + " it ; (1)



r region r dummy Risk_Z it 1 + it (2)

where Risk_A it and Risk_Z it stand, interchangeably, for country’s i sovereign

and bank risk. Within regression (1) the feedback e¤ect from one risk

to the other is allowed to depend on the speci…c periods described in Table

1. In turn, within regression (2) the coe¢ cients are allowed to di¤er di¤er between

core and peripheral countries. The results are presented in Table 2 in the

Appendix. The European crisis period (January 2010-August 2012) featured

a particularly large degree of pass-through from bank risk into sovereign risk.

Notably, feedback loops are not too di¤erent in peripheral and core countries.

If anything, bank risk has a stronger pass-through e¤ect on sovereign risk in peripheral

economies. Overall, there is some evidence of the correlation between

risk indicators having diverged across time and regions. The rest of the paper

attempts to connect this time and spatial variation in risk to the dynamics of

the underlying macroeconomic conditions.

4 Econometric Analysis

This section presents a panel data model of the feedback loop for each risk

variable. 23 As in Thukral (2013) or Heinz and Sun (2014), the starting point is

22 To complement the data description, Table A1 in the Appendix presents summary statistics

for the full sample and for the core and periphery subsamples.

23 The low number of observations calls for pooling country data to take advantage of both

time series and cross-country variation and for keeping the model as parsimonious as possible.


a Generalized Least Squares (GLS) estimator, using the CDS variables in levels.

Following the literature, in addition to the risk indicators, the model controls

for …nancial, global, macroeconomic, and contagion e¤ects:

Risk_A it = Ai + ZA Risk_Z it 1 + AA X A it 1 + ZA X Z it 1 + GA X G it 1 + " A it

Within this framework, the coe¢ cient ZA measures the extent to which

Risk Z feeds into Risk A. In addition, the model controls for the primary

determinants of Risk A (Xit A) and Risk Z (XZ it ). When dealing with the sovereign

risk model, Xit A collects the macro variables and XZ it collects the banking

sector variables. When dealing with the bank risk model, this reverses. The

variable Ai collects country-speci…c characteristics. Euro area sovereign debt

markets have been subject to recurrent bouts of dramatic co-movement during

the crisis, which a number of commentators have associated with contagion. 24

This cross-sectional correlation can bias the standard errors, making the estimations

less reliable. To address this issue the model controls for global and

contagion factors (Xit G ). To gauge the relative importance role of the di¤erent

sets of covariates, they are included and discussed in steps.

Additionally, the high degree of persistence of the CDS series raises concerns

about the robustness of the results. To address this concern the model

incorporates dynamic e¤ects by including a lag of the dependent variable,

(1 A L)Risk_A it = Ai + ZA Risk_Z it 1 + X it 1 + " A it

where L is the lag operator, A is the autoregressive coe¢ cient of Risk A,

= [ AA; ZA; GA] and X it 1 = [Xit A 1 ; XZ it 1 ; XG it 1 ]. The bias (Nickel bias)

introduced by the dynamic element is tackled by using system-GMM (Arellano

and Bover, 1995), which relies on the use of internal instruments (lagged levels

and di¤erences of the endogenous and predetermined variables).

4.1 Sovereign Risk Model

In a …rst step, similar to D’Agostino and Ehrmann (2014), the model only uses

the macro factors. The variables included are: debt to GDP, …scal balance,

…nancial account, GDP growth, unemployment and in‡ation. 25 The results

(Column 1, Table 3) are broadly in line with previous literature. Remarkably,

the …scal balance shows no signi…cant relation with sovereign risk. Next, to

assess the importance of banking factors for the pricing of sovereign risk, the

model also includes the bank risk determinants. Following the literature, the regressors

include: loan quality (non-performing loans to total loans), pro…tability

Signi…cant gaps in Greek data preclude its use on the econometric part.

24 According to Alter and Beyer (2013) or Broto and Perez-Quiros (2013) contagion played a

non-negligible role in peripheral countries. Heinz and Sun (2014) …nd that shocks to Spanish

and Italian CDS delivered the largest spillovers.

25 The Breusch-Pagan Lagrange Multiplier test strongly supported the inclusion of random



(return on assets), bank capital (tangible common equity ratio), the home bias

in the banks’portfolio (domestic assets as a % of total assets), the exposure to

public entities (private assets over total assets) and a measure of funding stability

(assets to deposits). The results, in column 2, serve as test for the …nancial

dominance hypothesis (Thuckar, 2013). While banking variables heavily in‡uence

the behavior of sovereign risk, converse to Thuckar (2013), macroeconomic

factors play a dominant role. 26

The next step adds BankRisk it to the framework. The coe¢ cient associated

with the bank risk indicator measures the feedback from bank into sovereign risk.

Column 3 presents the results for this model. There is a positive and signi…cant

relation between bank and sovereign risk. For every 10 basis points (bps) increase

in bank risk, sovereign risk increases by 4.2 bps in the following month.

This is a large degree of pass-through. To lower the degree of commonality in the

error terms, the model also controls for global shocks and potential contagion

e¤ects. 27 To proxy contagion, the model includes the average of the sovereign

CDS for other euro area countries. In turn, the model includes the VIX index to

proxy for global shocks. Column 4 from Table 3 presents the results. While the

VIX Index does not appear to have a signi…cant relation to sovereign risk, the

contagion indicator presents a highly signi…cant positive relation with sovereign

risk. Controlling for global and contagion e¤ects does not alter the signi…cance

of pass-through, although the size of the coe¢ cient becomes smaller (3.1 bps

increase in sovereign risk for every 10 bps increase on bank risk). 28

Finally, column 5 presents a dynamic version of the sovereign risk model.

As detailed above, the model is estimated using system GMM. 29 The dynamic

element is large (close to unity) and highly signi…cant. Remarkably, while the

pass-through from bank to sovereign risk remains signi…cant, the sign reverses.

According to the results, for every 10 bps increase in bank risk, sovereign risk

decreases by 0.9 bps.

4.2 Bank Risk Model

Following similar steps, the bank-related variables are included …rst. Next, the

macroeconomic controls are introduced. Global shocks are again proxied with

the VIX. Instead, contagion e¤ects are now accounted for using the Itraxx Junior

index. Finally, the dynamic version of the model, including the lagged value of

bank risk, is estimated. Table 4 presents the results for these models.

As shown in Columns 1 and 2 of Table 4, banks with a larger home bias

and larger private sector credit face larger bank risk. Non-performing loans are

associated, as expected, with higher bank risk. Interestingly, a lower ratio of

26 The regression’s R-squared increases by more than 50% after adding the bank variables,

but still gives macro factors a larger weight in explaining the sovereign risk variance.

27 A Pesaran test on the model´s residuals shows a signi…cant degree of spatial correlation.

28 The results (available under request) using a two-step Driscoll-Kraay correction for crosssectional

correlation are almost undistinguishable.

29 Both the Sargan endogeneity tests and the Di¤erence-in-Hansen tests of exogeneity tests

validate the instruments.


assets to deposits and higher bank capital are associated with larger levels of

stress. This result could be re‡ecting the fact that banks located in countries

with stronger sovereigns have less need to build their own capital cushions (as

in De Grauwe and Ji, 2013). 30 Column 3 shows the results for the model

including the lagged value of sovereign risk. The feedback coe¢ cient is, again,

highly signi…cant (0.53). In turn, as expected, larger values for the Itraxx

and VIX Indices associate with more bank risk (column 4). Contagion across

banks is a signi…cant phenomenon. Finally, column 5 of Table 4 presents the

estimates for the dynamic model of bank risk. The coe¢ cient of main interest,

the one associated with the sovereign risk indicator, is positive and signi…cant.

According to the results, a 10 bps increase in sovereign risk leads to a 0.8 bps

increase in bank risk.

4.3 A cheat impulse-response

Combining the pass-through coe¢ cients obtained from the sovereign and bank

risk models, one can recoup the dynamic response of sovereign and bank risk

to shocks to one another. The …gures below present a graphical representation

of shocking such system of equations with a 50 bps shock to sovereign risk (left

chart) and to bank risk (right chart).

Figures 3.1 and 3.2 illustrate the di¤erent form that average feedback e¤ects

take. On the one hand, there is a strong positive feedback arising from sovereign

shocks (Figure 3.1). On the other, there is no evidence of a feedback loop from

bank risk into sovereign risk. Quite the opposite, bank risk shocks induce a

milder and negative reaction of sovereign risk (Figure 3.2).

30 In unreported estimates using the Driscoll-Kraay correction, the results are qualitatively



5 Digging into the Sources of Feedback Loops

The relation between both risks might depend on the underlying economic and

…nancial environment. For instance, according to Acharya et al. (2013) or Martin

et al. (2014), explicit and implicit balance sheet interrelations can powerfully

amplify feedback loops. This section tests what conditions a¤ect the intensity

of the pass through by incorporating interactions between the risk measure and

other variables,

(1 A L)Risk_A it = Ai + ZA Risk_Z it 1 + F ZA F it 1 Risk_Z it 1 + X it 1 +" A it

where F it 1 is the factor interacting with the Risk Z. Within this framework,

the feedback between risks becomes:

@Risk_A it

@Risk_Z it 1

= ZA + F ZA F it 1

The sovereign risk model with interactions is estimated for the following

variables: size of the banking system (Gennaioli al., 2014), banks’foreign liabilities

(Cavallo and Izquierdo, 2009) and banks’non-performing loans (Acharya

et al., 2013). 31 In turn, the candidate variables for a¤ecting the feedback from

the sovereign to the banks are public debt to GDP (Mody and Sandry, 2011),

banks’balance sheet exposure to the sovereign (Angeloni and Wol¤, 2012), and

the investment grade status of sovereign debt (Correa et al., 2012). Table 5

(sovereign risk) and Table 6 (bank risk) contain the result.

Table 5 vindicates the validity of most of the above-mentioned channels

of transmission. It shows that the three interactions present signi…cant positive

spillovers from bank to sovereign risk. The pass-through of risk becomes

stronger where the volume of non-performing loans and banks’foreign liabilities

are larger. Conversely, there is no evidence that, where banks have bigger

balance sheets, the feedback e¤ect is stronger.

In turn, Table 6 shows that the feedback from sovereign into bank risk is

stronger the larger the stock of public debt and larger banking system exposure

to the sovereign. The results also show a signi…cantly stronger pass-through of

sovereign risk when the sovereign rating is below investment grade. 32 When a

sovereign rating falls outside the investment grade category, it loses a large pool

of potential investors, a¤ecting negatively sovereign risk.

5.1 Economic signi…cance

To grasp the economic relevance of these results, Figures 4.1 and 4.2 depict

various e¤ects in basis points (bps). Figure 4.1 shows how the pass-through onto

31 All the variables are measured as percentage of GDP to make them relative to the authorities’potential.

32 This is despite the fact that the adjustments to the ECB’s collateral policy during the

crisis (Eberl and Webber, 2014) ameliorated the impact of not having an investment grade.


sovereign risk of a 100 bps increase in bank risk depends on di¤erent values of

F it . Figure 4.2 does the same for the e¤ect on bank risk of a 100 bps increase in

sovereign risk. The …gures compare the e¤ects at the minimum and maximum

values within sample of the corresponding indicators.

Some of the conditional risk dynamics are economically very sizeable. For

instance, Figure 4.1 shows that a 100 bps increase in bank risk does not lead

to a positive feedback on sovereign risk even if the banking system size is at

its maximum within the sample. The feedback is, instead, very large when the

asset quality of the banks, as measured by the share of non-performing loans

(NPLs), is high. While for the lowest level of NPLs there is no positive feedback

e¤ect, at the maximum value within sample, the e¤ect is well above 150 bps.

Similarly, when banks’foreign liabilities are large, there is a sizeable positive

feedback e¤ect of bank risk to sovereign risk. In turn, Figure 4.2 shows the

relevance of the balance sheet exposure to the sovereign in the transmission

of stress. Faced with an increase in sovereign risk of 100 bps, banking systems

holding the lowest level of exposure face an 18 bps increase in their risk. Instead,

banks with larger exposures face an increase of 80 bps. The feedback e¤ect can

also grow considerably in the presence of large public debt stock (up to 62 bps),

and when the sovereign has lost its investment grade (40 bps).

6 Bank Rescues and the Feedback Loop

This section uses the sovereign risk model to assess quantitatively the e¤ect

that bank rescue operations can have on the feedback from bank into sovereign

risk. According to Acharya et al. (2013), the rescue packages enacted by euro

area governments to …ght o¤ the …nancial crisis generated a risk transfer. As

sovereigns began to support their banks, investors became more con…dent about

banks. This led to a lowering of banks’CDS spreads. Unfortunately, in some

cases, the weight governments had to lift pushed up sovereign risk, facilitating


the emergence of a perverse feedback loop. 33 To limit extreme forms of this risk

transfer, the euro area authorities devised a tool to assist banks directly using

the European Stability Mechanism (ESM, 2014). 34 Implementing this policy

requires determining when a sovereign might not be able to do it on its own.

The analysis focuses on direct exposures and contingent liabilities. 35

Figure 5 provides a dynamic representation of the e¤ects of a shock to bank

risk when the sovereign has bailed out the banks using an amount equal to the

average …scal cost of bank crises (15% of GDP) in Laeven and Valencia (2011).

In line with Acharya et al. (2013) risk-transfer hypothesis, the results, presented

in Table 7, point to a signi…cantly larger pass-through of bank risk into

the sovereign for those economies where the authorities more heavily supported

their banking system. According to the results, given a size of the bailout equal

to 15% of GDP, for every 100 bps increase in bank risk, sovereign risk increases

by 11 bps within a year. As shown in columns 3 and 4, this e¤ect becomes more

sizeable for countries where the banks have a larger amount of foreign liabilities

or a larger balance sheet exposure to the sovereign.

7 Conclusions and Policy implications

This paper has analyzed the factors associated with the emergence of perverse

spirals of sovereign and bank stress. Using a dynamic panel data model, it

uncovers underlying vulnerabilities that reinforce the process where shocks to

33 Alter and Beyer (2013) …nd that, in Spain, the nationalization of Bankia led to an increase

on spillovers.

34 Direct recapitalisation is provided if a sovereign cannot provide support without triggering

a …scal crisis.

35 The data, in an annual format, comes from the European Commission.


a country´s …scal health contaminate the …nancial sector. Countries where

public debt is larger, and where domestic banks have a larger exposure to their

own sovereign, face stronger feedback loops from sovereign into bank risk. The

same goes for countries losing their investment grade status. On the other, the

analysis also identi…ed factors associated with an elevated transmission of bank

distress to the sovereign. In countries where banks are larger, funded with more

foreign credit and face more non-performing loans, the feedback from bank risk

into sovereign risk is stronger.

From an economic policy perspective, these results can help in monitoring

the build-up of …scal weaknesses and the robustness of the …nancial system to

…scal shocks. Additionally, the new framework to handle banking crises in the

euro area implies that, if the foreseen bail-in of the bank’s private creditors is

not enough, individual banks could be rescued directly by the o¢ cial sector.

For such direct recapitalization to happen, it has to be the case that the country

could endanger its sustainability if supporting the bank alone. This paper

informs this process by studying the circumstances in which …nancial rescues

might overburden the sovereign.


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Data Series Source Frequency

Local currency rating Fitch Monthly

Harmonized CPI Index Haver Analytics Monthly

Nominal GDP Haver Analytics Quarterly

Financial Account Balance Haver Analytics Quarterly

Harmonized Unemployment Rate Haver Analytics Monthly

General Government

Nonconsolidated Debt

Variables included in the analysis: Main features

Haver Analytics


General Government: Net


Haver analytics


Banking System Balance Sheet Haver Analytics Monthly

VIX index CBOE Monthly

Itraxx Junior Financial Indices Bloomberg Monthly

Central Bank Lending Individual Central Banks Monthly

Bank rescue operations (liabilities

and contingent liabilities)

European Comission


Sovereign 5-year CDS spreads Bloomberg and Datastream Monthly

Bank 5-year CDS spreads Bloomberg and Datastream Monthly

Figure 1: Sovereign and Bank Risk in the Euro Area



1000 1500 2000



1000 1500 2000


100 200 300 400


100 200 300 400

Figure 2: A bird’s eye view of Sovereign and Bank risk

Austria Belgium France

2005m1 2010m1 2015m1



2005m1 2010m1 2015m1 2005m1 2010m1 2015m1







2005m9 2014m1 2005m9 2014m1



Variable Observations Mean Std. Dev. Min Max Observations Mean Std. Dev. Min Max Observations Mean Std. Dev. Min Max

Sovereign CDS 497 55.18 59.88 1.30 329.28 450 263.28 525.55 1.76 6882.40 947 154.07 379.19 1.30 6882.40

Bank CDS Index 505 134.83 87.57 7.93 431.49 471 399.27 436.90 8.10 2067.82 976 262.45 336.83 7.93 2067.82

Public Debt (% GDP) 485 86.23 17.64 50.21 118.93 485 94.61 35.58 25.62 183.29 970 90.42 28.38 25.62 183.29

GDP Growth 470 0.65 0.68 -1.52 1.56 470 0.17 1.19 -2.87 2.86 940 0.41 1.00 -2.87 2.86

Fiscal Balance (% GDP) 467 -2.70 3.88 -13.85 7.52 485 -6.90 7.12 -40.31 8.69 952 -4.84 6.13 -40.31 8.69

Inflation 500 1.99 1.05 -1.64 5.77 500 2.07 1.64 -2.92 5.68 1000 2.03 1.38 -2.92 5.77

Unemployment 500 6.71 2.21 3.00 11.30 498 12.28 5.91 4.20 27.80 998 9.49 5.25 3.00 27.80

Financial account (% GDP) 485 -2.96 3.97 -10.24 5.28 485 5.31 4.64 -6.75 13.80 970 1.17 5.98 -10.24 13.80

Central Bank Liquidity (% GDP) 485 0.07 0.04 0.01 0.37 485 0.20 0.21 0.00 0.86 970 0.13 0.16 0.00 0.86

Bank Size (% GDP) 485 4.09 0.48 3.20 5.05 485 5.04 2.96 2.03 12.95 970 4.56 2.17 2.03 12.95

Bank access to Central Bank

(% of total assets)

Bank exposure to General

Government (% total assets)

Bank foreign liabilities (%

total assets)

Table A1. Summary statistics by geographical area: Core versus periphery

Core Periphery Full Sample

495 0.02 0.01 0.00 0.08 495 0.04 0.05 0.00 0.24 990 0.03 0.04 0.00 0.24

495 0.07 0.02 0.04 0.14 495 0.06 0.03 0.02 0.12 990 0.06 0.02 0.02 0.14

495 0.14 0.10 0.04 0.42 495 0.12 0.13 0.02 0.44 990 0.13 0.12 0.02 0.44

Bank Home Bias 495 0.76 0.12 0.44 0.87 495 0.83 0.19 0.42 0.96 990 0.79 0.16 0.42 0.96

Non-performing loans 315 2.95 0.93 0.51 4.37 321 8.91 6.08 0.75 29.37 636 5.96 5.29 0.51 29.37

Return On Assets 291 0.27 0.30 -1.31 0.74 312 0.16 1.51 -9.52 8.11 603 0.21 1.10 -9.52 8.11

Capital ratio 291 14.43 2.40 10.47 19.64 321 11.73 3.09 -2.89 20.29 612 13.01 3.09 -2.89 20.29

VIX Index 505 21.47 10.13 10.31 68.51 505 21.47 10.13 10.31 68.51 1010 21.47 10.13 10.31 68.51

Itraxx Junior 505 189.70 134.92 12.70 529.63 505 189.70 134.92 12.70 529.63 1010 189.70 134.85 12.70 529.63

Data runs from September 2007 until January 2014. Core countries are Germany, France, Belgium, Austria and Netherlands. Periphery countries include Ireland, Italy, Portugal, Greece and Spain.

Table 2. Bank and Sovereign risk loops by periods and regions

Dep. Var: Sovereign Risk Full Sample Core vs Periphery

Bank Risk Index (during Period 1)

Bank Risk Index (during Period 2)

Bank Risk Index (during Period 3)

Bank Risk Index (during Period 4)

Bank Risk Index (during Period 5)

Bank Risk Index (if core country)

Bank Risk Index (if peripheral country)















Constant 2.77e+01** 6.64

[11.48] [18.48]

0 0

Observations 890 890

R-squared 0.57 0.47

Dep. Var: Bank Risk

Full Sample

Core vs Periphery

Sovereign Risk (during Period 1)

Sovereign Risk (during Period 2)

Sovereign Risk (during Period 3)

Sovereign Risk (during Period 4)

Sovereign Risk (during Period 5)

Sovereign Risk (if core country)

Sovereign Risk (if peripheral country)















Constant 7.46e+01*** 9.93e+01

[13.09] [61.77]

Observations 887 887

R-squared 0.53 0.49

Standard errors in brackets. *** p

Table 3: Sovereign Risk Determinants

Macro factors Financial Dominance? Including Bank Risk Contagion & Global Dynamic Panel - GMM

Public Debt (% GDP) 2.85074*** 3.03412*** 4.36466** 2.41267 -0.16140

[0.89192] [0.56061] [2.09820] [2.07418] [0.20781]

Inflation 41.88270** 42.31815*** 29.64377** 22.61933 0.44141

Fiscal Balance (% GDP) -0.89429 -0.55020

[20.46094] [5.00052] [14.16609] [14.55856] [1.41064]

[2.11210] [1.26736]

Unemployment 24.68218** -12.20295*** -10.53978 -7.01676 -0.31433

[12.53592] [2.09126] [8.34691] [8.04618] [0.26713]

Financial account (% GDP) 8.77482** 7.80787*** 7.17697*** 8.14635*** 1.09265*

[4.14930] [1.20988] [1.80998] [1.64103] [0.58133]

GDP Growth -17.52449 -18.99286*** -14.83255 -18.74828 6.17804**

[23.69710] [7.22454] [20.55788] [21.74692] [2.41609]

VIX Index -0.91588 0.39047**

[0.82917] [0.19799]

Other EA Sovereigns shock 0.39342*** 0.00061

[0.11429] [0.00676]

Sovereign Risk 1.01593***


Bank Risk 0.39870*** 0.31215*** -0.07841***

[0.12477] [0.11033] [0.00990]

Bank Home Bias 11.00246 236.51432** 78.24319 -50.41017***

[59.50422] [92.86427] [107.01573] [15.36646]

Banks Private Assets 160.95964*** 107.23422* 58.04025 15.34203***

[18.25573] [57.96847] [54.51904] [4.45689]

Banks Assets to Deposits -236.85313*** -163.62463 -32.00543 -14.99333

[65.08768] [146.28101] [114.79124] [19.15749]

Banks funding from CB 5,198.06913*** 4,112.84212*** 4,519.80440*** -223.87460

[470.39164] [1,407.27665] [1,441.52373] [149.90186]

Non-performing loans 12.12065*** -7.57912 -3.34707 2.47547*

[2.26705] [7.11940] [7.22369] [1.27734]

Banks ROA 47.70184** 31.68372 31.27290 -14.87771**

[18.74357] [51.60539] [45.46797] [7.28199]

Banks' capital -3.18224 -12.52783 -15.01924

[3.26946] [13.04944] [13.30132]

Constant -412.95925*** -393.23746*** -474.62623*** -251.14994 33.93331

[97.57750] [104.77950] [147.34333] [169.74052] [30.87990]

Observations 819 543 543 543 534

R-squared 0.38 0.69 0.57 0.64

Sargan Test 163.8

Robust standard errors in brackets. *** p

TABLE 4: Bank Risk Determinants

Bank factors- ECB data

Bank factors - ECB & IMF


Bank & macro factors Including Sovereign Risk Contagion& Global Dynamic Panel - GMM

Bank Home Bias 645.29571*** -716.28140*** -593.35841*** -603.49704*** -632.80719*** -98.82991***

[145.31905] [72.68564] [59.86096] [51.56742] [50.16786] [17.39234]

Banks Private Assets 152.65238*** 159.25283*** 145.29523*** 61.91358*** 69.18806*** 16.86370*

[18.11861] [14.43979] [18.07193] [16.72809] [16.77667] [9.94408]

Banks Assets to Deposits -543.65014*** -68.74126 -204.20386*** -83.56846 -44.39201 -28.38901

[104.23514] [79.97998] [66.36337] [57.84573] [56.27863] [23.93555]

Banks funding from CB 6,595.00077*** 2,684.92650*** 2,528.43335*** -458.79641 324.59844 -194.33941**

[446.91417] [494.04588] [476.17691] [465.16681] [448.88955] [98.64389]

Non-performing loans 35.35430*** 48.81510*** 41.70719*** 43.35807*** 5.90579***

[2.61304] [2.31106] [2.05799] [1.94035] [0.54551]

Return on Assets 56.36885*** 19.19785 -16.29750 21.99971 -11.52246**

[19.51918] [18.90258] [16.48943] [16.14227] [5.11996]

Banks Capital 19.45874*** 20.19696*** 20.11718*** 21.42841*** 1.38871

[3.58217] [3.33560] [2.87317] [2.79773] [1.71676]

VIX Index 3.33184*** 0.74053***

[0.53468] [0.25618]

Itraxx Junior Index 0.22615*** -0.01302

[0.05723] [0.01891]

Bank Risk 0.84649***

Sovereign Risk 0.53566*** 0.43689*** 0.09855*


[0.03935] [0.04121] [0.05741]

Public Debt (% GDP) -3.37438*** -4.94036*** -4.46404*** -0.68918*

[0.56867] [0.50316] [0.51088] [0.41107]

Inflation 29.39950*** 6.72022 2.28081 -0.64970

[4.94414] [4.57294] [4.39152] [3.40228]

Unemployment -4.23686** 2.03623 2.90225 0.23715

[2.12738] [1.88949] [1.79895] [0.83246]

Financial account (% GDP) 2.40681** -1.23244 -1.74867* -0.07521

[1.20956] [1.07562] [1.03529] [0.30885]

GDP Growth -3.79514 9.13444 22.17504*** 10.21375***

[7.36727] [6.41658] [6.21383] [2.93831]

Constant -74.18393 -76.50793 295.24515*** 538.73613*** 256.14497*** 97.26295***

[181.85684] [121.33788] [106.88495] [93.78800] [98.68833] [34.31770]

873 543 543 543 543 534

Number of Observations 873 543 543 543 543 543

R-squared 0.31 0.79 0.84 0.87 .

Sargan Test 176.04

Robust standard errors in brackets. *** p

Table 5. Channels of transmission of Bank Risk

Dep. variable: Sovereign Risk

Bank Size



Bank foreign


Bank Risk -0.06955*** -0.06905*** -0.06800***

Bank Risk* Banks' Size 0.00071***

Bank Risk* Non-performing


Bank Risk*Banks' Foreign


[0.01003] [0.01025] [0.01041]






Constant 81.07124*** 79.63427** 80.33793**

[31.02581] [31.08578] [31.29246]

Other controls Yes Yes Yes

Observations 534 534 534

Number of countries 9 9 9

Robust standard errors in brackets. *** p

Bank Risk -0.01432*** -0.01259*** -0.01180*** -0.01273***

Bank Risk* Bailout Size

(including contingent claims)

Table 7. Bank bailouts and feedback loops

[0.00319] [0.00258] [0.00275] [0.00247]



Dep. variable: Sovereign Risk

Bank Risk* Bailout Size 0.22592***


Bank Risk*Bailout Sizes*Banks'

Foreign Liabilities


Bank Risk*Bailout Size*Banks'

sovereign exposure




Constant -1.04590 -1.68672 12.64554*** -1.83684

[3.80581] [3.47236] [3.56339] [3.41779]

Other controls Yes Yes Yes Yes

Observations 534 534 534 534

Number of countries 9 9 9 9

Robust standard errors in brackets. *** p

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©European Stability Mechanism 09/2015

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